Clustering Based Resource Planning, Work Assignment, and Cross-Skill Training Planning in Services Management

Abstract

An embodiment of the invention provides a method for service management, wherein resources that have performed tasks in at least two of a first category, a second category, and at least one additional category are identified. A plurality of correlation sums are determined where the correlation sum includes at least two categories, wherein the correlation sums are added together to produce a correlation value. A correlation product for each correlation sum is calculated based on the respective correlation sum and the number of resources that have performed tasks with respect to the correlation sum. A quotient is calculated for each correlation sum based on the respective correlation product and the correlation value. The categories are grouped into clusters with a clustering module based on the quotients; and, resources are associated with the clusters based on task performance history of the resources.

What is claimed is: 
     
         1 . A system for service management, said system comprising:
 a task identifier for identifying resources that have performed tasks in at least two of a first category, a second category, and at least one additional category, said task identifier further identifies:
 a lowest number of tasks performed between the first and second categories for each of the identified resources that have performed tasks in the first and second categories, 
 a lowest number of tasks performed between the first and additional categories for each of the identified resources that have performed tasks in the first and additional categories, and 
 a lowest number of tasks performed between the second and additional categories for each of the identified resources that have performed tasks in the second and additional categories; 
   a computer processor connected to said task identifier, said computer processor calculates:
 a first sum by summing the lowest number of tasks performed between the first and second categories for all of the identified resources, 
 a second sum by summing the lowest number of tasks performed between the first and additional categories for all of the identified resources, 
 a third sum by summing the lowest number of tasks performed between the second and additional categories for all of the identified resources, 
 a first product by multiplying the first sum by a number of resources that have performed tasks in the first and second categories, 
 a second product by multiplying the second sum by a number of resources that have performed tasks in the first and additional categories, 
 a third product by multiplying the third sum by a number of resources that have performed tasks in the second and additional categories, and 
 first, second and third quotients by dividing each of the first, second and third products by a sum of the first, second, and third sums; and 
   a clustering module connected to said computer processor, said clustering module
 groups the categories into a number of clusters based on the first, second and third quotients using an agglomerative clustering approach, and 
 associates resources with the clusters based on task performance history of the resources. 
   
     
     
         2 . The system according to  claim 1 , wherein the first quotient indicates a similarity between the first and second categories, the second quotient indicates a similarity between the first and additional categories, and the third quotient indicates a similarity between the second and additional categories. 
     
     
         3 . The system according to  claim 1 , wherein the task performance history of the resources includes:
 a number of tasks in the first category that each resource has performed,   a number of tasks in the second category that each resource has performed, and   a number of tasks in the at least one additional category that each resource has performed.   
     
     
         4 . The system according to  claim 1 , further comprising a cluster analyzer connected to at least one of said task identifier, said computer processor, and said clustering module, said cluster analyzer:
 determines a level of belongingness of a resource to a cluster based on a number of tasks the resource has performed in all categories within the cluster and a number of tasks the resource has performed in other categories of other clusters, and   determines the resource's level of experience to the cluster based on a number of tasks the resource has performed in all categories within the cluster and the total number of tasks that belong to the categories within the cluster.   
     
     
         5 . The system according to  claim 4 , further comprising a resource analyzer connected to at least one of said task identifier, said computer processor, said clustering module, and said cluster analyzer, said resource analyzer generates a ranked list of resources that have capability to perform a new task, said resource analyzer:
 identifies a cluster that contains a category that the new task belongs to,   ranks resources in the cluster based on belongingness, availability, and experience of the resources to the cluster, and   assigns the new task to the resource on the top of the ranked list.   
     
     
         6 . The system according to  claim 1 , further comprising a resource analyzer connected to at least one of said task identifier, said computer processor, and said clustering module, said resource analyzer:
 identifies at least one category in a cluster that at least one resource belongs to the cluster at a certain belongingness level, lacks experience in, and   recommends the at least one category to the at least one resource for cross-skill training.   
     
     
         7 . The system according to  claim 1 , wherein said clustering module determines an optimal number of clusters during the agglomerative clustering process based on variation coefficient analysis, said clustering module:
 generates a number of clustering arrangements, each of the clustering arrangements including a different number of clusters;   for each cluster in a clustering arrangement, measures:
 an average variation coefficient of categories of the cluster, and 
 a variation coefficient of the cluster; 
   measures a gain of variation coefficient of the cluster by differencing the average variation coefficient of the categories and the variation coefficient of the cluster;   averages the gain of variation coefficients for all clusters in each of the clustering arrangements; and   selects a clustering arrangement that has a smallest gain of variation coefficient as an optimal clustering arrangement.   
     
     
         8 . A system for service management, said system comprising:
 a task identifier for identifying resources that have performed tasks in at least two of a first category, a second category, and at least one additional category;   a computer processor connected to said task identifier, said computer processor:
 determines a plurality of correlation sums where the correlation sum includes at least two categories, 
 adds the correlation sums together to produce a correlation value; 
 calculates a correlation product for each correlation sum based on the respective correlation sum and a number of tasks used to determine the respective correlation sum, and 
 calculates a quotient for each correlation sum based on the respective correlation product and the correlation value; and 
   a clustering module connected to said computer processor, said clustering module groups the categories into clusters based on the quotients and associates resources with the clusters based on task performance history of the resources.   
     
     
         9 . The system according to  claim 8 , wherein the quotients include:
 a first quotient indicating a similarity between first and second categories;   a second quotient indicating a similarity between the first category and at least one additional category; and   a third quotient indicating a similarity between the second and additional categories.   
     
     
         10 . The system according to  claim 9 , wherein the task performance history of the resources includes:
 a number of tasks in the first category that each resource has performed,   a number of tasks in the second category that each resource has performed, and   a number of tasks in the at least one additional category that each resource has performed.   
     
     
         11 . The system according to  claim 8 , further comprising a cluster analyzer connected to at least one of said task identifier, said computer processor, and said clustering module, said cluster analyzer:
 determines a level of belongingness of a resource to a cluster based on a number of tasks the resource has performed in all categories within the cluster and a number of tasks the resource has performed in other categories of other clusters, and   determines the resource's level of experience to the cluster based on a number of tasks the resource has performed in all categories within the cluster.   
     
     
         12 . The system according to  claim 11 , further comprising a resource analyzer connected to at least one of said task identifier, said computer processor, said clustering module, and said cluster analyzer, said resource analyzer generates a ranked list of resources that have capability to perform a new task, said resource analyzer:
 identifies a cluster that contains a category that the new task belongs to; and   ranks resources in the cluster based on belongingness, availability, and experience of the resources to the cluster.   
     
     
         13 . The system according to  claim 8 , further comprising a resource analyzer connected to at least one of said task identifier, said computer processor, and said clustering module, said resource analyzer:
 identifies at least one category in a cluster that at least one resource belongs to the cluster at a certain belongingness level, lacks experience in, and   recommends the at least one category to the at least one resource for cross-skill training.   
     
     
         14 . A system comprising:
 a task identifier for identifying resources that have performed tasks in at least two of a first category, a second category, and at least one additional category,
 said task identifier further identifies, for each of the identified resources, a number of tasks in the first category that the resource has performed, a number of tasks in the second category that the resource has performed, and a number of tasks in the at least one additional category that the resource has performed, 
 for each of the identified resources that have performed tasks in the first category and the second category, said task identifier identifies a lowest value between the number of tasks in the first category the resource has performed and the number of tasks in the second category that the resource has performed, 
 for each of the identified resources that have performed tasks in the first category and the at least one additional category, said task identifier identifies a lowest value between the number of tasks in the first category the resource has performed and the number of tasks in the at least one additional category that the resource has performed, 
 for each of the identified resources that have performed tasks in the second category and the at least one additional category, said task identifier identifies a lowest value between the number of tasks in the second category the resource has performed and the number of tasks in the at least one additional category that the resource has performed, 
   a computer processor connected to said task identifier, said computer processor calculates:
 a first sum of the lowest values of the number of tasks in the first category the resource has performed and the number of tasks in the second category that the resource has performed for all of the identified resources that have performed tasks in the first category and the second category, 
 a second sum of the lowest values of the number of tasks in the first category the resource has performed and the number of tasks in the at least one additional category that the resource has performed for all of the identified resources that have performed tasks in the first category and the at least one additional category, 
 a third sum of the lowest values of the number of tasks in the second category the resource has performed and the number of tasks in the at least one additional category that the resource has performed for all of the identified resources that have performed tasks in the second category and the at least one additional category, 
 a first product of the first sum multiplied by a number of resources that have performed tasks in the first and second categories, a second product of the second sum multiplied by a number of resources that have performed tasks in the first and additional categories, and a third product of the third sum multiplied by a number of resources that have performed tasks in the second and additional categories, 
 a fourth sum of the first sum, the second sum, and the third sum, 
 a first quotient of the first product divided by the fourth sum, the first quotient being the similarity between the first category and the second category, 
 a second quotient of the second product divided by the fourth sum, the second quotient being the similarity between the first category and the at least one additional category, and 
 a third quotient of the third product divided by the fourth sum, the third quotient being the similarity between the second category and the at least one additional category; and 
   a clustering module connected to said computer processor, said clustering module
 groups the categories into clusters based on the first quotient, the second quotient, and the third quotient, and 
 associates resources with the clusters based on task performance history of the resources. 
   
     
     
         15 . The system according to  claim 14 , wherein the task performance history of the resources includes, for each of the identified resources:
 the number of tasks in the first category that the resource has performed,   the number of tasks in the second category that the resource has performed, and   the number of tasks in the at least one additional category that the resource has performed.   
     
     
         16 . The system according to  claim 14 , further comprising a resource analyzer connected to at least one of said task identifier, said computer processor, and said clustering module, said resource analyzer identifies at least one category in a cluster that a resource at least one of lacks skill in and lacks experience in. 
     
     
         17 . The system according to  claim 14 , further comprising a cluster analyzer connected to at least one of said task identifier, said computer processor, and said clustering module, said cluster analyzer determines:
 a level of compatibility of a resource to a cluster based on a number of tasks the resource has performed in all categories within the cluster, and   the resource's level of experience to a category within the cluster based on a number of tasks the resource has performed in the category.   
     
     
         18 . The system according to  claim 17 , further comprising a resource analyzer connected to at least one of said task identifier, said computer processor, said clustering module, and said cluster analyzer, said resource analyzer generates a ranked list of resources that have capability to perform a new task based on experience of the resources to categories within the cluster. 
     
     
         19 . The system according to  claim 14 , wherein said clustering module determines an optimal number of clusters during an agglomerative clustering process based on variation coefficient analysis, said clustering module:
 generates a number of clustering arrangements, each of the clustering arrangements including a different number of clusters;   measures, for each cluster in a clustering arrangement:
 an average variation coefficient of categories of the cluster, and 
 a variation coefficient of the cluster; 
   measures a gain of variation coefficient of the cluster by differencing the average variation coefficient of the categories and the variation coefficient of the cluster;   averages the gain of variation coefficients for all clusters in each of the clustering arrangements; and   selects a clustering arrangement that has a smallest gain of variation coefficient as an optimal clustering arrangement.   
     
     
         20 . A computer program product for service management, said computer program product comprising:
 a computer readable storage medium;   first program instructions to identify resources that have performed tasks in at least two of a first category, a second category, and at least one additional category;   second program instructions to determine a plurality of correlation sums where the correlation sum includes at least two categories;   third program instructions to add the correlation sums together to produce a correlation value;   fourth program instructions to calculate a correlation product for each correlation sum based on the respective correlation sum and a number of tasks used to determine the respective correlation sum;   fifth program instructions to calculate a quotient for each correlation sum based on the respective correlation product and the correlation value;   sixth program instructions to group the categories into clusters with a clustering module based on the quotients; and   seventh program instructions to associate resources with the clusters with the clustering module based on task performance history of the resources,   said first program instructions, said second program instructions, said third program instructions, said fourth program instructions, said fifth program instructions, said sixth program instructions, and said seventh program instructions are stored on said computer readable storage medium.   
     
     
         21 . The computer program product according to  claim 20 , wherein said second program instructions:
 determines similarities between the first category and the second category by, for each of the identified resources that have performed tasks in the first category and the second category:
 identifying a lowest value between a number of tasks in the first category the resource has performed and a number of tasks in the second category that the resource has performed, and 
 calculating a first sum of the lowest values of the number of tasks in the first category the resource has performed and the number of tasks in the second category that the resource has performed; 
   determines similarities between the first category and the at least one additional category by, for each of the identified resources that have performed tasks in the first category and the at least one additional category:
 identifying a lowest value between the number of tasks in the first category the resource has performed and a number of tasks in the at least one additional category that the resource has performed, and 
 calculating a second sum of the lowest values of the number of tasks in the first category the resource has performed and the number of tasks in the at least one additional category that the resource has performed; and 
   determines similarities between the second category and the at least one additional category by, for each of the identified resources that have performed tasks in the second category and the at least one additional category:
 identifying a lowest value between the number of tasks in the second category the resource has performed and the number of tasks in the at least one additional category that the resource has performed, and 
 calculating a third sum of the lowest values of the number of tasks in the second category the resource has performed and the number of tasks in the at least one additional category that the resource has performed. 
   
     
     
         22 . The computer program product according to  claim 21 , wherein said fourth program instructions calculates:
 a first product of the first sum multiplied by a number of resources that have performed tasks in the first and second categories,   a second product of the second sum multiplied by a number of resources that have performed tasks in the first and additional categories,   a third product of the third sum multiplied by a number of resources that have performed tasks in the second and additional categories.   
     
     
         23 . The computer program product according to  claim 22 , wherein said fifth program instructions calculates:
 a fourth sum of the first sum, the second sum, and the third sum;   a first quotient of the first product divided by the fourth sum, the first quotient being the similarity between the first category and the second category;   a second quotient of the second product divided by the fourth sum, the second quotient being the similarity between the first category and the at least one additional category; and   a third quotient of the third product divided by the fourth sum, the third quotient being the similarity between the second category and the at least one additional category.   
     
     
         24 . The computer program product according to  claim 20 , further comprising:
 eighth program instructions to determine a level of compatibility of a resource to a cluster based on a number of tasks the resource has performed in all categories within the cluster; and   ninth program instructions to determine the resource's level of experience to a category within the cluster based on a number of tasks the resource has performed in the category.   
     
     
         25 . The computer program product according to  claim 20 , further comprising tenth program instructions to generate a ranked list of resources that have capability to perform a new task based on experience of the resources to categories within the cluster.